Overview

Dataset statistics

Number of variables86
Number of observations49102
Missing cells2680692
Missing cells (%)63.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory31.2 MiB
Average record size in memory667.0 B

Variable types

Categorical14
DateTime1
Boolean43
Numeric28

Warnings

study_no has a high cardinality: 8108 distinct values High cardinality
icd_code has a high cardinality: 124 distinct values High cardinality
age is highly correlated with heightHigh correlation
dbp is highly correlated with sbpHigh correlation
gcs_eye_movement is highly correlated with gcs_verbal_responseHigh correlation
gcs_motor_response is highly correlated with gcs_verbal_response and 1 other fieldsHigh correlation
gcs_verbal_response is highly correlated with gcs_eye_movement and 1 other fieldsHigh correlation
haematocrit_percent is highly correlated with gcs_motor_responseHigh correlation
height is highly correlated with ageHigh correlation
lymphocytes_percent is highly correlated with neutrophils_percentHigh correlation
neutrophils is highly correlated with wbcHigh correlation
neutrophils_percent is highly correlated with lymphocytes_percentHigh correlation
sbp is highly correlated with dbpHigh correlation
wbc is highly correlated with neutrophilsHigh correlation
day_from_enrolment is highly correlated with day_from_onsetHigh correlation
day_from_onset is highly correlated with day_from_enrolmentHigh correlation
abdominal_tenderness has 41002 (83.5%) missing values Missing
agitated has 49096 (> 99.9%) missing values Missing
albumin has 41020 (83.5%) missing values Missing
alt has 41020 (83.5%) missing values Missing
ascites has 38935 (79.3%) missing values Missing
ast has 41020 (83.5%) missing values Missing
bleeding has 20543 (41.8%) missing values Missing
bleeding_gi has 48919 (99.6%) missing values Missing
bleeding_gum has 48919 (99.6%) missing values Missing
bleeding_nose has 48919 (99.6%) missing values Missing
bleeding_urine has 48919 (99.6%) missing values Missing
bleeding_vensite has 48919 (99.6%) missing values Missing
body_temperature has 17515 (35.7%) missing values Missing
breath has 21567 (43.9%) missing values Missing
care_type has 47536 (96.8%) missing values Missing
cns_abnormal has 49096 (> 99.9%) missing values Missing
cns_abnormal_signs has 49097 (> 99.9%) missing values Missing
compression has 47536 (96.8%) missing values Missing
conjunctival_injection has 41002 (83.5%) missing values Missing
creatine_kinase has 41020 (83.5%) missing values Missing
cryoprecipitate has 47536 (96.8%) missing values Missing
crystalloid has 47536 (96.8%) missing values Missing
dbp has 48992 (99.8%) missing values Missing
dehydration has 47536 (96.8%) missing values Missing
ffp has 47536 (96.8%) missing values Missing
gum_packing has 47536 (96.8%) missing values Missing
haematocrit_high has 47536 (96.8%) missing values Missing
haematocrit_no has 47536 (96.8%) missing values Missing
haematocrit_percent has 39443 (80.3%) missing values Missing
hepatomegaly has 41002 (83.5%) missing values Missing
icd_code has 45601 (92.9%) missing values Missing
igg has 45041 (91.7%) missing values Missing
igg_interpretation has 45041 (91.7%) missing values Missing
igm has 45041 (91.7%) missing values Missing
igm_interpretation has 45041 (91.7%) missing values Missing
jaundice has 49000 (99.8%) missing values Missing
lethargy has 21567 (43.9%) missing values Missing
liver_acute has 49087 (> 99.9%) missing values Missing
lymphocytes has 41006 (83.5%) missing values Missing
lymphocytes_percent has 41030 (83.6%) missing values Missing
monocytes has 41006 (83.5%) missing values Missing
monocytes_percent has 41006 (83.5%) missing values Missing
movement has 43502 (88.6%) missing values Missing
nasal_packing has 47536 (96.8%) missing values Missing
neutrophils has 41007 (83.5%) missing values Missing
neutrophils_percent has 41030 (83.6%) missing values Missing
ns1_platelia_interpretation has 43218 (88.0%) missing values Missing
ns1_trip_control_interpretation has 41001 (83.5%) missing values Missing
ns1_trip_test_interpretation has 41001 (83.5%) missing values Missing
pcr_dengue_load has 41003 (83.5%) missing values Missing
platelet_no has 47536 (96.8%) missing values Missing
platelets has 47536 (96.8%) missing values Missing
pleural_effusion has 38935 (79.3%) missing values Missing
plt has 39443 (80.3%) missing values Missing
pulse has 48990 (99.8%) missing values Missing
pulse_status has 48988 (99.8%) missing values Missing
rbc has 47536 (96.8%) missing values Missing
restlessness has 21567 (43.9%) missing values Missing
sbp has 48992 (99.8%) missing values Missing
serology_interpretation has 47062 (95.8%) missing values Missing
skin_clammy has 21567 (43.9%) missing values Missing
wbc has 41006 (83.5%) missing values Missing
day_from_admission has 38802 (79.0%) missing values Missing
alt is highly skewed (γ1 = 58.52623694) Skewed
ast is highly skewed (γ1 = 27.71801641) Skewed
monocytes is highly skewed (γ1 = 88.04653391) Skewed
pcr_dengue_load is highly skewed (γ1 = 41.74880205) Skewed
day_from_enrolment is highly skewed (γ1 = -32.77291566) Skewed
day_from_onset is highly skewed (γ1 = -32.77232447) Skewed
study_no is uniformly distributed Uniform
pcr_dengue_load has 6032 (12.3%) zeros Zeros
day_from_enrolment has 8100 (16.5%) zeros Zeros
day_from_admission has 1594 (3.2%) zeros Zeros
day_from_onset has 8100 (16.5%) zeros Zeros

Reproduction

Analysis started2021-02-12 10:45:11.280725
Analysis finished2021-02-12 10:48:10.252469
Duration2 minutes and 58.97 seconds
Software versionpandas-profiling v2.10.0
Download configurationconfig.yaml

Variables

study_no
Categorical

HIGH CARDINALITY
UNIFORM

Distinct8108
Distinct (%)16.5%
Missing0
Missing (%)0.0%
Memory size383.7 KiB
1-2088
 
12
6-0999
 
11
6-0259
 
11
3-1171
 
11
3-0721
 
11
Other values (8103)
49046 

Length

Max length7
Median length6
Mean length6.171764898
Min length5

Characters and Unicode

Total characters303046
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)< 0.1%

Sample

1st row1-0001
2nd row1-0001
3rd row1-0001
4th row1-0001
5th row1-0001
ValueCountFrequency (%)
1-208812
 
< 0.1%
6-099911
 
< 0.1%
6-025911
 
< 0.1%
3-117111
 
< 0.1%
3-072111
 
< 0.1%
6-020510
 
< 0.1%
4-039410
 
< 0.1%
6-014810
 
< 0.1%
3-053510
 
< 0.1%
3-093310
 
< 0.1%
Other values (8098)48996
99.8%
2021-02-12T10:48:10.486943image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1-208812
 
< 0.1%
6-099911
 
< 0.1%
6-025911
 
< 0.1%
3-117111
 
< 0.1%
3-072111
 
< 0.1%
6-020510
 
< 0.1%
4-039410
 
< 0.1%
6-014810
 
< 0.1%
3-053510
 
< 0.1%
3-093310
 
< 0.1%
Other values (8098)48996
99.8%

Most occurring characters

ValueCountFrequency (%)
053051
17.5%
-49102
16.2%
148697
16.1%
625556
8.4%
325290
8.3%
223407
7.7%
421741
7.2%
517441
 
5.8%
713240
 
4.4%
812941
 
4.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number253944
83.8%
Dash Punctuation49102
 
16.2%

Most frequent character per category

ValueCountFrequency (%)
053051
20.9%
148697
19.2%
625556
10.1%
325290
10.0%
223407
9.2%
421741
8.6%
517441
 
6.9%
713240
 
5.2%
812941
 
5.1%
912580
 
5.0%
ValueCountFrequency (%)
-49102
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common303046
100.0%

Most frequent character per script

ValueCountFrequency (%)
053051
17.5%
-49102
16.2%
148697
16.1%
625556
8.4%
325290
8.3%
223407
7.7%
421741
7.2%
517441
 
5.8%
713240
 
4.4%
812941
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII303046
100.0%

Most frequent character per block

ValueCountFrequency (%)
053051
17.5%
-49102
16.2%
148697
16.1%
625556
8.4%
325290
8.3%
223407
7.7%
421741
7.2%
517441
 
5.8%
713240
 
4.4%
812941
 
4.3%

date
Date

Distinct1488
Distinct (%)3.0%
Missing1
Missing (%)< 0.1%
Memory size383.7 KiB
Minimum2010-10-17 00:00:00
Maximum2019-04-13 00:00:00
2021-02-12T10:48:10.608561image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:48:10.754286image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct2
Distinct (%)< 0.1%
Missing8
Missing (%)< 0.1%
Memory size383.7 KiB
False
45153 
True
 
3941
(Missing)
 
8
ValueCountFrequency (%)
False45153
92.0%
True3941
 
8.0%
(Missing)8
 
< 0.1%
2021-02-12T10:48:10.829803image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

abdominal_tenderness
Boolean

MISSING

Distinct2
Distinct (%)< 0.1%
Missing41002
Missing (%)83.5%
Memory size383.7 KiB
False
7963 
True
 
137
(Missing)
41002 
ValueCountFrequency (%)
False7963
 
16.2%
True137
 
0.3%
(Missing)41002
83.5%
2021-02-12T10:48:10.870724image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

age
Real number (ℝ≥0)

HIGH CORRELATION

Distinct16
Distinct (%)< 0.1%
Missing63
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean6.492913803
Minimum0
Maximum15
Zeros23
Zeros (%)< 0.1%
Memory size383.7 KiB
2021-02-12T10:48:10.929852image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median6
Q39
95-th percentile13
Maximum15
Range15
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.652470081
Coefficient of variation (CV)0.5625317372
Kurtosis-0.814106663
Mean6.492913803
Median Absolute Deviation (MAD)3
Skewness0.3538625918
Sum318406
Variance13.3405377
MonotocityNot monotonic
2021-02-12T10:48:11.022728image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
54880
9.9%
44752
9.7%
34613
9.4%
24365
8.9%
74285
8.7%
64062
8.3%
83932
8.0%
93542
7.2%
13393
6.9%
103059
 
6.2%
Other values (6)8156
16.6%
ValueCountFrequency (%)
023
 
< 0.1%
13393
6.9%
24365
8.9%
34613
9.4%
44752
9.7%
ValueCountFrequency (%)
15470
 
1.0%
141311
2.7%
131721
3.5%
122027
4.1%
112604
5.3%

agitated
Boolean

MISSING

Distinct2
Distinct (%)33.3%
Missing49096
Missing (%)> 99.9%
Memory size383.7 KiB
False
 
4
True
 
2
(Missing)
49096 
ValueCountFrequency (%)
False4
 
< 0.1%
True2
 
< 0.1%
(Missing)49096
> 99.9%
2021-02-12T10:48:11.089471image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

albumin
Real number (ℝ≥0)

MISSING

Distinct212
Distinct (%)2.6%
Missing41020
Missing (%)83.5%
Infinite0
Infinite (%)0.0%
Mean44.07483296
Minimum14.8
Maximum55.1
Zeros0
Zeros (%)0.0%
Memory size383.7 KiB
2021-02-12T10:48:11.173497image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum14.8
5-th percentile39
Q142.1
median44.1
Q346.1
95-th percentile49.1
Maximum55.1
Range40.3
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.123851523
Coefficient of variation (CV)0.07087608309
Kurtosis1.522828316
Mean44.07483296
Median Absolute Deviation (MAD)2
Skewness-0.2377124208
Sum356212.8
Variance9.758448339
MonotocityNot monotonic
2021-02-12T10:48:11.304074image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
44.7139
 
0.3%
44.1134
 
0.3%
43120
 
0.2%
44118
 
0.2%
45.3116
 
0.2%
44.4115
 
0.2%
44.9114
 
0.2%
43.8112
 
0.2%
43.3111
 
0.2%
44.5110
 
0.2%
Other values (202)6893
 
14.0%
(Missing)41020
83.5%
ValueCountFrequency (%)
14.81
< 0.1%
27.11
< 0.1%
29.31
< 0.1%
29.61
< 0.1%
301
< 0.1%
ValueCountFrequency (%)
55.11
< 0.1%
551
< 0.1%
54.71
< 0.1%
54.52
< 0.1%
53.82
< 0.1%

alt
Real number (ℝ≥0)

MISSING
SKEWED

Distinct201
Distinct (%)2.5%
Missing41020
Missing (%)83.5%
Infinite0
Infinite (%)0.0%
Mean24.14486513
Minimum1
Maximum4827
Zeros0
Zeros (%)0.0%
Memory size383.7 KiB
2021-02-12T10:48:11.429438image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9
Q113
median17
Q323
95-th percentile54
Maximum4827
Range4826
Interquartile range (IQR)10

Descriptive statistics

Standard deviation62.66078558
Coefficient of variation (CV)2.595201308
Kurtosis4329.699993
Mean24.14486513
Median Absolute Deviation (MAD)5
Skewness58.52623694
Sum195138.8
Variance3926.374049
MonotocityNot monotonic
2021-02-12T10:48:11.561940image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16561
 
1.1%
14548
 
1.1%
15540
 
1.1%
17501
 
1.0%
12499
 
1.0%
13498
 
1.0%
18429
 
0.9%
19379
 
0.8%
11354
 
0.7%
20289
 
0.6%
Other values (191)3484
 
7.1%
(Missing)41020
83.5%
ValueCountFrequency (%)
13
 
< 0.1%
25
 
< 0.1%
314
< 0.1%
412
 
< 0.1%
530
0.1%
ValueCountFrequency (%)
48271
< 0.1%
16391
< 0.1%
5941
< 0.1%
5681
< 0.1%
4511
< 0.1%

anorexia
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size48.1 KiB
False
48753 
True
 
349
ValueCountFrequency (%)
False48753
99.3%
True349
 
0.7%
2021-02-12T10:48:11.640505image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

ascites
Boolean

MISSING

Distinct2
Distinct (%)< 0.1%
Missing38935
Missing (%)79.3%
Memory size383.7 KiB
False
9998 
True
 
169
(Missing)
38935 
ValueCountFrequency (%)
False9998
 
20.4%
True169
 
0.3%
(Missing)38935
79.3%
2021-02-12T10:48:11.678767image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

ast
Real number (ℝ≥0)

MISSING
SKEWED

Distinct246
Distinct (%)3.0%
Missing41020
Missing (%)83.5%
Infinite0
Infinite (%)0.0%
Mean50.18431081
Minimum9
Maximum2529
Zeros0
Zeros (%)0.0%
Memory size383.7 KiB
2021-02-12T10:48:11.758502image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile27
Q136
median43
Q353
95-th percentile86
Maximum2529
Range2520
Interquartile range (IQR)17

Descriptive statistics

Standard deviation54.23689926
Coefficient of variation (CV)1.080754092
Kurtosis1116.669763
Mean50.18431081
Median Absolute Deviation (MAD)8
Skewness27.71801641
Sum405589.6
Variance2941.641241
MonotocityNot monotonic
2021-02-12T10:48:11.893716image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
39298
 
0.6%
41295
 
0.6%
42289
 
0.6%
36286
 
0.6%
44280
 
0.6%
40270
 
0.5%
37261
 
0.5%
43257
 
0.5%
45256
 
0.5%
35256
 
0.5%
Other values (236)5334
 
10.9%
(Missing)41020
83.5%
ValueCountFrequency (%)
91
 
< 0.1%
111
 
< 0.1%
132
< 0.1%
141
 
< 0.1%
153
< 0.1%
ValueCountFrequency (%)
25291
< 0.1%
24141
< 0.1%
17401
< 0.1%
7901
< 0.1%
7531
< 0.1%

bleeding
Boolean

MISSING

Distinct2
Distinct (%)< 0.1%
Missing20543
Missing (%)41.8%
Memory size383.7 KiB
False
27440 
True
 
1119
(Missing)
20543 
ValueCountFrequency (%)
False27440
55.9%
True1119
 
2.3%
(Missing)20543
41.8%
2021-02-12T10:48:11.967289image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

bleeding_gi
Boolean

MISSING

Distinct2
Distinct (%)1.1%
Missing48919
Missing (%)99.6%
Memory size383.7 KiB
True
 
134
False
 
49
(Missing)
48919 
ValueCountFrequency (%)
True134
 
0.3%
False49
 
0.1%
(Missing)48919
99.6%
2021-02-12T10:48:12.007973image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

bleeding_gum
Boolean

MISSING

Distinct2
Distinct (%)1.1%
Missing48919
Missing (%)99.6%
Memory size383.7 KiB
False
 
152
True
 
31
(Missing)
48919 
ValueCountFrequency (%)
False152
 
0.3%
True31
 
0.1%
(Missing)48919
99.6%
2021-02-12T10:48:12.042011image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing8
Missing (%)< 0.1%
Memory size383.7 KiB
False
47978 
True
 
1116
(Missing)
 
8
ValueCountFrequency (%)
False47978
97.7%
True1116
 
2.3%
(Missing)8
 
< 0.1%
2021-02-12T10:48:12.076559image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

bleeding_nose
Boolean

MISSING

Distinct2
Distinct (%)1.1%
Missing48919
Missing (%)99.6%
Memory size383.7 KiB
False
 
127
True
 
56
(Missing)
48919 
ValueCountFrequency (%)
False127
 
0.3%
True56
 
0.1%
(Missing)48919
99.6%
2021-02-12T10:48:12.113982image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing8
Missing (%)< 0.1%
Memory size383.7 KiB
False
46530 
True
 
2564
(Missing)
 
8
ValueCountFrequency (%)
False46530
94.8%
True2564
 
5.2%
(Missing)8
 
< 0.1%
2021-02-12T10:48:12.147519image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

bleeding_urine
Boolean

MISSING

Distinct2
Distinct (%)1.1%
Missing48919
Missing (%)99.6%
Memory size383.7 KiB
False
 
175
True
 
8
(Missing)
48919 
ValueCountFrequency (%)
False175
 
0.4%
True8
 
< 0.1%
(Missing)48919
99.6%
2021-02-12T10:48:12.181307image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

bleeding_vensite
Boolean

MISSING

Distinct2
Distinct (%)1.1%
Missing48919
Missing (%)99.6%
Memory size383.7 KiB
False
 
160
True
 
23
(Missing)
48919 
ValueCountFrequency (%)
False160
 
0.3%
True23
 
< 0.1%
(Missing)48919
99.6%
2021-02-12T10:48:12.217316image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

body_temperature
Real number (ℝ≥0)

MISSING

Distinct83
Distinct (%)0.3%
Missing17515
Missing (%)35.7%
Infinite0
Infinite (%)0.0%
Mean37.38209168
Minimum27.5
Maximum41.3
Zeros0
Zeros (%)0.0%
Memory size383.7 KiB
2021-02-12T10:48:12.292882image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum27.5
5-th percentile36.3
Q136.8
median37
Q338
95-th percentile39
Maximum41.3
Range13.8
Interquartile range (IQR)1.2

Descriptive statistics

Standard deviation0.905022785
Coefficient of variation (CV)0.02421006274
Kurtosis0.9625254408
Mean37.38209168
Median Absolute Deviation (MAD)0.5
Skewness0.8468410394
Sum1180788.13
Variance0.8190662415
MonotocityNot monotonic
2021-02-12T10:48:12.420565image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
377863
16.0%
36.52444
 
5.0%
36.82263
 
4.6%
381824
 
3.7%
37.51500
 
3.1%
36.71261
 
2.6%
36.91064
 
2.2%
391059
 
2.2%
38.51021
 
2.1%
36961
 
2.0%
Other values (73)10327
21.0%
(Missing)17515
35.7%
ValueCountFrequency (%)
27.51
 
< 0.1%
291
 
< 0.1%
34.551
 
< 0.1%
34.71
 
< 0.1%
353
< 0.1%
ValueCountFrequency (%)
41.31
 
< 0.1%
41.12
 
< 0.1%
413
< 0.1%
40.95
< 0.1%
40.84
< 0.1%

breath
Boolean

MISSING

Distinct2
Distinct (%)< 0.1%
Missing21567
Missing (%)43.9%
Memory size383.7 KiB
False
27526 
True
 
9
(Missing)
21567 
ValueCountFrequency (%)
False27526
56.1%
True9
 
< 0.1%
(Missing)21567
43.9%
2021-02-12T10:48:12.497679image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

care_type
Categorical

MISSING

Distinct3
Distinct (%)0.2%
Missing47536
Missing (%)96.8%
Memory size383.7 KiB
Level 3
1011 
Level 2
451 
Level 1
104 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters10962
Distinct characters8
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLevel 1
2nd rowLevel 3
3rd rowLevel 3
4th rowLevel 2
5th rowLevel 3
ValueCountFrequency (%)
Level 31011
 
2.1%
Level 2451
 
0.9%
Level 1104
 
0.2%
(Missing)47536
96.8%
2021-02-12T10:48:12.666899image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-02-12T10:48:12.739202image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
level1566
50.0%
31011
32.3%
2451
 
14.4%
1104
 
3.3%

Most occurring characters

ValueCountFrequency (%)
e3132
28.6%
L1566
14.3%
v1566
14.3%
l1566
14.3%
1566
14.3%
31011
 
9.2%
2451
 
4.1%
1104
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter6264
57.1%
Uppercase Letter1566
 
14.3%
Space Separator1566
 
14.3%
Decimal Number1566
 
14.3%

Most frequent character per category

ValueCountFrequency (%)
e3132
50.0%
v1566
25.0%
l1566
25.0%
ValueCountFrequency (%)
31011
64.6%
2451
28.8%
1104
 
6.6%
ValueCountFrequency (%)
L1566
100.0%
ValueCountFrequency (%)
1566
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin7830
71.4%
Common3132
 
28.6%

Most frequent character per script

ValueCountFrequency (%)
e3132
40.0%
L1566
20.0%
v1566
20.0%
l1566
20.0%
ValueCountFrequency (%)
1566
50.0%
31011
32.3%
2451
 
14.4%
1104
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII10962
100.0%

Most frequent character per block

ValueCountFrequency (%)
e3132
28.6%
L1566
14.3%
v1566
14.3%
l1566
14.3%
1566
14.3%
31011
 
9.2%
2451
 
4.1%
1104
 
0.9%

cns_abnormal
Boolean

MISSING

Distinct2
Distinct (%)33.3%
Missing49096
Missing (%)> 99.9%
Memory size383.7 KiB
True
 
5
False
 
1
(Missing)
49096 
ValueCountFrequency (%)
True5
 
< 0.1%
False1
 
< 0.1%
(Missing)49096
> 99.9%
2021-02-12T10:48:12.788284image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

cns_abnormal_signs
Boolean

MISSING

Distinct2
Distinct (%)40.0%
Missing49097
Missing (%)> 99.9%
Memory size383.7 KiB
False
 
4
True
 
1
(Missing)
49097 
ValueCountFrequency (%)
False4
 
< 0.1%
True1
 
< 0.1%
(Missing)49097
> 99.9%
2021-02-12T10:48:12.838437image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

compression
Boolean

MISSING

Distinct2
Distinct (%)0.1%
Missing47536
Missing (%)96.8%
Memory size383.7 KiB
False
 
1562
True
 
4
(Missing)
47536 
ValueCountFrequency (%)
False1562
 
3.2%
True4
 
< 0.1%
(Missing)47536
96.8%
2021-02-12T10:48:12.880715image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing41002
Missing (%)83.5%
Memory size383.7 KiB
False
7276 
True
 
824
(Missing)
41002 
ValueCountFrequency (%)
False7276
 
14.8%
True824
 
1.7%
(Missing)41002
83.5%
2021-02-12T10:48:12.928622image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

creatine_kinase
Real number (ℝ≥0)

MISSING

Distinct522
Distinct (%)6.5%
Missing41020
Missing (%)83.5%
Infinite0
Infinite (%)0.0%
Mean129.4042316
Minimum2
Maximum4677
Zeros0
Zeros (%)0.0%
Memory size383.7 KiB
2021-02-12T10:48:13.029961image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile54
Q178
median102
Q3134
95-th percentile251
Maximum4677
Range4675
Interquartile range (IQR)56

Descriptive statistics

Standard deviation165.1682439
Coefficient of variation (CV)1.276374365
Kurtosis258.2658739
Mean129.4042316
Median Absolute Deviation (MAD)27
Skewness13.08702085
Sum1045845
Variance27280.5488
MonotocityNot monotonic
2021-02-12T10:48:13.165611image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
112107
 
0.2%
92103
 
0.2%
94101
 
0.2%
88100
 
0.2%
7899
 
0.2%
9194
 
0.2%
9792
 
0.2%
7792
 
0.2%
10091
 
0.2%
8189
 
0.2%
Other values (512)7114
 
14.5%
(Missing)41020
83.5%
ValueCountFrequency (%)
21
< 0.1%
41
< 0.1%
51
< 0.1%
131
< 0.1%
171
< 0.1%
ValueCountFrequency (%)
46771
< 0.1%
42341
< 0.1%
41881
< 0.1%
38411
< 0.1%
34611
< 0.1%

cryoprecipitate
Boolean

MISSING

Distinct2
Distinct (%)0.1%
Missing47536
Missing (%)96.8%
Memory size383.7 KiB
False
 
1563
True
 
3
(Missing)
47536 
ValueCountFrequency (%)
False1563
 
3.2%
True3
 
< 0.1%
(Missing)47536
96.8%
2021-02-12T10:48:13.239689image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

crystalloid
Boolean

MISSING

Distinct2
Distinct (%)0.1%
Missing47536
Missing (%)96.8%
Memory size383.7 KiB
False
 
1553
True
 
13
(Missing)
47536 
ValueCountFrequency (%)
False1553
 
3.2%
True13
 
< 0.1%
(Missing)47536
96.8%
2021-02-12T10:48:13.279341image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

dbp
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct10
Distinct (%)9.1%
Missing48992
Missing (%)99.8%
Infinite0
Infinite (%)0.0%
Mean73.45454545
Minimum40
Maximum100
Zeros0
Zeros (%)0.0%
Memory size383.7 KiB
2021-02-12T10:48:13.339437image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum40
5-th percentile60
Q170
median75
Q380
95-th percentile90
Maximum100
Range60
Interquartile range (IQR)10

Descriptive statistics

Standard deviation10.50874373
Coefficient of variation (CV)0.1430645805
Kurtosis0.2522280296
Mean73.45454545
Median Absolute Deviation (MAD)5
Skewness-0.3243628955
Sum8080
Variance110.4336947
MonotocityNot monotonic
2021-02-12T10:48:13.440401image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
8032
 
0.1%
7030
 
0.1%
6018
 
< 0.1%
9011
 
< 0.1%
759
 
< 0.1%
503
 
< 0.1%
853
 
< 0.1%
652
 
< 0.1%
1001
 
< 0.1%
401
 
< 0.1%
(Missing)48992
99.8%
ValueCountFrequency (%)
401
 
< 0.1%
503
 
< 0.1%
6018
< 0.1%
652
 
< 0.1%
7030
0.1%
ValueCountFrequency (%)
1001
 
< 0.1%
9011
 
< 0.1%
853
 
< 0.1%
8032
0.1%
759
 
< 0.1%

dehydration
Boolean

MISSING

Distinct2
Distinct (%)0.1%
Missing47536
Missing (%)96.8%
Memory size383.7 KiB
False
 
1517
True
 
49
(Missing)
47536 
ValueCountFrequency (%)
False1517
 
3.1%
True49
 
0.1%
(Missing)47536
96.8%
2021-02-12T10:48:13.505250image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

diarrhoea
Boolean

Distinct2
Distinct (%)< 0.1%
Missing13
Missing (%)< 0.1%
Memory size383.7 KiB
False
46306 
True
 
2783
(Missing)
 
13
ValueCountFrequency (%)
False46306
94.3%
True2783
 
5.7%
(Missing)13
 
< 0.1%
2021-02-12T10:48:13.546033image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

ffp
Boolean

MISSING

Distinct2
Distinct (%)0.1%
Missing47536
Missing (%)96.8%
Memory size383.7 KiB
False
 
1554
True
 
12
(Missing)
47536 
ValueCountFrequency (%)
False1554
 
3.2%
True12
 
< 0.1%
(Missing)47536
96.8%
2021-02-12T10:48:13.585740image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

gcs_eye_movement
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size383.7 KiB
nan
49097 
1.0
 
3
3.0
 
1
4.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters147306
Distinct characters7
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rownan
2nd rownan
3rd rownan
4th rownan
5th rownan
ValueCountFrequency (%)
nan49097
> 99.9%
1.03
 
< 0.1%
3.01
 
< 0.1%
4.01
 
< 0.1%
2021-02-12T10:48:13.752017image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-02-12T10:48:13.820030image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
nan49097
> 99.9%
1.03
 
< 0.1%
3.01
 
< 0.1%
4.01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
n98194
66.7%
a49097
33.3%
.5
 
< 0.1%
05
 
< 0.1%
13
 
< 0.1%
41
 
< 0.1%
31
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter147291
> 99.9%
Decimal Number10
 
< 0.1%
Other Punctuation5
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
05
50.0%
13
30.0%
41
 
10.0%
31
 
10.0%
ValueCountFrequency (%)
n98194
66.7%
a49097
33.3%
ValueCountFrequency (%)
.5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin147291
> 99.9%
Common15
 
< 0.1%

Most frequent character per script

ValueCountFrequency (%)
.5
33.3%
05
33.3%
13
20.0%
41
 
6.7%
31
 
6.7%
ValueCountFrequency (%)
n98194
66.7%
a49097
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII147306
100.0%

Most frequent character per block

ValueCountFrequency (%)
n98194
66.7%
a49097
33.3%
.5
 
< 0.1%
05
 
< 0.1%
13
 
< 0.1%
41
 
< 0.1%
31
 
< 0.1%

gcs_motor_response
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size383.7 KiB
nan
49097 
1.0
 
2
2.0
 
1
5.0
 
1
4.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters147306
Distinct characters8
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rownan
2nd rownan
3rd rownan
4th rownan
5th rownan
ValueCountFrequency (%)
nan49097
> 99.9%
1.02
 
< 0.1%
2.01
 
< 0.1%
5.01
 
< 0.1%
4.01
 
< 0.1%
2021-02-12T10:48:14.005584image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-02-12T10:48:14.066855image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
nan49097
> 99.9%
1.02
 
< 0.1%
2.01
 
< 0.1%
5.01
 
< 0.1%
4.01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
n98194
66.7%
a49097
33.3%
.5
 
< 0.1%
05
 
< 0.1%
12
 
< 0.1%
21
 
< 0.1%
41
 
< 0.1%
51
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter147291
> 99.9%
Decimal Number10
 
< 0.1%
Other Punctuation5
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
05
50.0%
12
 
20.0%
21
 
10.0%
41
 
10.0%
51
 
10.0%
ValueCountFrequency (%)
n98194
66.7%
a49097
33.3%
ValueCountFrequency (%)
.5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin147291
> 99.9%
Common15
 
< 0.1%

Most frequent character per script

ValueCountFrequency (%)
.5
33.3%
05
33.3%
12
 
13.3%
21
 
6.7%
41
 
6.7%
51
 
6.7%
ValueCountFrequency (%)
n98194
66.7%
a49097
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII147306
100.0%

Most frequent character per block

ValueCountFrequency (%)
n98194
66.7%
a49097
33.3%
.5
 
< 0.1%
05
 
< 0.1%
12
 
< 0.1%
21
 
< 0.1%
41
 
< 0.1%
51
 
< 0.1%

gcs_verbal_response
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size383.7 KiB
nan
49097 
1.0
 
3
5.0
 
1
4.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters147306
Distinct characters7
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rownan
2nd rownan
3rd rownan
4th rownan
5th rownan
ValueCountFrequency (%)
nan49097
> 99.9%
1.03
 
< 0.1%
5.01
 
< 0.1%
4.01
 
< 0.1%
2021-02-12T10:48:14.248607image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-02-12T10:48:14.305836image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
nan49097
> 99.9%
1.03
 
< 0.1%
5.01
 
< 0.1%
4.01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
n98194
66.7%
a49097
33.3%
.5
 
< 0.1%
05
 
< 0.1%
13
 
< 0.1%
41
 
< 0.1%
51
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter147291
> 99.9%
Decimal Number10
 
< 0.1%
Other Punctuation5
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
05
50.0%
13
30.0%
41
 
10.0%
51
 
10.0%
ValueCountFrequency (%)
n98194
66.7%
a49097
33.3%
ValueCountFrequency (%)
.5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin147291
> 99.9%
Common15
 
< 0.1%

Most frequent character per script

ValueCountFrequency (%)
.5
33.3%
05
33.3%
13
20.0%
41
 
6.7%
51
 
6.7%
ValueCountFrequency (%)
n98194
66.7%
a49097
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII147306
100.0%

Most frequent character per block

ValueCountFrequency (%)
n98194
66.7%
a49097
33.3%
.5
 
< 0.1%
05
 
< 0.1%
13
 
< 0.1%
41
 
< 0.1%
51
 
< 0.1%

gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing8
Missing (%)< 0.1%
Memory size383.7 KiB
Male
27628 
Female
21466 

Length

Max length6
Median length4
Mean length4.874485681
Min length4

Characters and Unicode

Total characters239308
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMale
2nd rowMale
3rd rowMale
4th rowMale
5th rowMale
ValueCountFrequency (%)
Male27628
56.3%
Female21466
43.7%
(Missing)8
 
< 0.1%
2021-02-12T10:48:14.488335image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-02-12T10:48:14.552348image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
male27628
56.3%
female21466
43.7%

Most occurring characters

ValueCountFrequency (%)
e70560
29.5%
a49094
20.5%
l49094
20.5%
M27628
 
11.5%
F21466
 
9.0%
m21466
 
9.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter190214
79.5%
Uppercase Letter49094
 
20.5%

Most frequent character per category

ValueCountFrequency (%)
e70560
37.1%
a49094
25.8%
l49094
25.8%
m21466
 
11.3%
ValueCountFrequency (%)
M27628
56.3%
F21466
43.7%

Most occurring scripts

ValueCountFrequency (%)
Latin239308
100.0%

Most frequent character per script

ValueCountFrequency (%)
e70560
29.5%
a49094
20.5%
l49094
20.5%
M27628
 
11.5%
F21466
 
9.0%
m21466
 
9.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII239308
100.0%

Most frequent character per block

ValueCountFrequency (%)
e70560
29.5%
a49094
20.5%
l49094
20.5%
M27628
 
11.5%
F21466
 
9.0%
m21466
 
9.0%

gum_packing
Boolean

MISSING

Distinct2
Distinct (%)0.1%
Missing47536
Missing (%)96.8%
Memory size383.7 KiB
False
 
1563
True
 
3
(Missing)
47536 
ValueCountFrequency (%)
False1563
 
3.2%
True3
 
< 0.1%
(Missing)47536
96.8%
2021-02-12T10:48:14.596468image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

haematocrit_high
Boolean

MISSING

Distinct2
Distinct (%)0.1%
Missing47536
Missing (%)96.8%
Memory size383.7 KiB
False
 
1455
True
 
111
(Missing)
47536 
ValueCountFrequency (%)
False1455
 
3.0%
True111
 
0.2%
(Missing)47536
96.8%
2021-02-12T10:48:14.632525image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

haematocrit_no
Real number (ℝ≥0)

MISSING

Distinct32
Distinct (%)2.0%
Missing47536
Missing (%)96.8%
Infinite0
Infinite (%)0.0%
Mean5.861430396
Minimum1
Maximum44
Zeros0
Zeros (%)0.0%
Memory size383.7 KiB
2021-02-12T10:48:14.713252image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q38
95-th percentile17
Maximum44
Range43
Interquartile range (IQR)6

Descriptive statistics

Standard deviation5.647071134
Coefficient of variation (CV)0.963428848
Kurtosis4.915606831
Mean5.861430396
Median Absolute Deviation (MAD)2
Skewness1.923294359
Sum9179
Variance31.88941239
MonotocityNot monotonic
2021-02-12T10:48:14.823298image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
1282
 
0.6%
2245
 
0.5%
3191
 
0.4%
4174
 
0.4%
5121
 
0.2%
690
 
0.2%
759
 
0.1%
948
 
0.1%
843
 
0.1%
1041
 
0.1%
Other values (22)272
 
0.6%
(Missing)47536
96.8%
ValueCountFrequency (%)
1282
0.6%
2245
0.5%
3191
0.4%
4174
0.4%
5121
0.2%
ValueCountFrequency (%)
441
 
< 0.1%
393
< 0.1%
321
 
< 0.1%
301
 
< 0.1%
292
< 0.1%

haematocrit_percent
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct251
Distinct (%)2.6%
Missing39443
Missing (%)80.3%
Infinite0
Infinite (%)0.0%
Mean38.44517755
Minimum12
Maximum60
Zeros0
Zeros (%)0.0%
Memory size383.7 KiB
2021-02-12T10:48:14.935068image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile32.8
Q135.9
median38.1
Q340.6
95-th percentile45
Maximum60
Range48
Interquartile range (IQR)4.7

Descriptive statistics

Standard deviation3.880491039
Coefficient of variation (CV)0.1009357034
Kurtosis1.477325504
Mean38.44517755
Median Absolute Deviation (MAD)2.4
Skewness0.5167785212
Sum371341.97
Variance15.0582107
MonotocityNot monotonic
2021-02-12T10:48:15.072641image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
39242
 
0.5%
40237
 
0.5%
37197
 
0.4%
38196
 
0.4%
41195
 
0.4%
42164
 
0.3%
36155
 
0.3%
44154
 
0.3%
43138
 
0.3%
35137
 
0.3%
Other values (241)7844
 
16.0%
(Missing)39443
80.3%
ValueCountFrequency (%)
121
< 0.1%
20.41
< 0.1%
211
< 0.1%
22.51
< 0.1%
241
< 0.1%
ValueCountFrequency (%)
602
 
< 0.1%
581
 
< 0.1%
571
 
< 0.1%
563
< 0.1%
557
< 0.1%

height
Real number (ℝ≥0)

HIGH CORRELATION

Distinct119
Distinct (%)0.2%
Missing8
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean119.036868
Minimum58
Maximum183
Zeros0
Zeros (%)0.0%
Memory size383.7 KiB
2021-02-12T10:48:15.199596image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum58
5-th percentile82
Q1101
median119
Q3136
95-th percentile158
Maximum183
Range125
Interquartile range (IQR)35

Descriptive statistics

Standard deviation23.08161849
Coefficient of variation (CV)0.1939031064
Kurtosis-0.7271770209
Mean119.036868
Median Absolute Deviation (MAD)17
Skewness0.04517110513
Sum5843996
Variance532.7611119
MonotocityNot monotonic
2021-02-12T10:48:15.334991image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1201142
 
2.3%
1151089
 
2.2%
1101006
 
2.0%
140965
 
2.0%
125918
 
1.9%
130892
 
1.8%
118843
 
1.7%
128831
 
1.7%
135827
 
1.7%
132793
 
1.6%
Other values (109)39788
81.0%
ValueCountFrequency (%)
586
< 0.1%
6012
< 0.1%
6111
< 0.1%
624
 
< 0.1%
6313
< 0.1%
ValueCountFrequency (%)
1836
 
< 0.1%
1796
 
< 0.1%
17614
 
< 0.1%
17516
< 0.1%
17439
0.1%

hepatomegaly
Boolean

MISSING

Distinct2
Distinct (%)< 0.1%
Missing41002
Missing (%)83.5%
Memory size383.7 KiB
False
8082 
True
 
18
(Missing)
41002 
ValueCountFrequency (%)
False8082
 
16.5%
True18
 
< 0.1%
(Missing)41002
83.5%
2021-02-12T10:48:15.411144image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

icd_code
Categorical

HIGH CARDINALITY
MISSING

Distinct124
Distinct (%)3.5%
Missing45601
Missing (%)92.9%
Memory size383.7 KiB
A91A
1350 
A91B
342 
A91.A
251 
B34
189 
J02
157 
Other values (119)
1212 

Length

Max length13
Median length4
Mean length4.000571265
Min length3

Characters and Unicode

Total characters14006
Distinct characters26
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17 ?
Unique (%)0.5%

Sample

1st rowA913
2nd rowJ02
3rd rowJ02
4th rowJ02
5th rowA912
ValueCountFrequency (%)
A91A1350
 
2.7%
A91B342
 
0.7%
A91.A251
 
0.5%
B34189
 
0.4%
J02157
 
0.3%
B349136
 
0.3%
A91C123
 
0.3%
A912103
 
0.2%
A91C163
 
0.1%
J0648
 
0.1%
Other values (114)739
 
1.5%
(Missing)45601
92.9%
2021-02-12T10:48:15.607427image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
a91a1350
38.5%
a91b342
 
9.8%
a91.a251
 
7.2%
b34189
 
5.4%
j02157
 
4.5%
b349136
 
3.9%
a91c123
 
3.5%
a912103
 
2.9%
a91c165
 
1.9%
j0648
 
1.4%
Other values (115)741
21.1%

Most occurring characters

ValueCountFrequency (%)
A4135
29.5%
92891
20.6%
12681
19.1%
B818
 
5.8%
0588
 
4.2%
3516
 
3.7%
.485
 
3.5%
4467
 
3.3%
J453
 
3.2%
2434
 
3.1%
Other values (16)538
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number7802
55.7%
Uppercase Letter5712
40.8%
Other Punctuation488
 
3.5%
Space Separator4
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
A4135
72.4%
B818
 
14.3%
J453
 
7.9%
C232
 
4.1%
K18
 
0.3%
N13
 
0.2%
D11
 
0.2%
L10
 
0.2%
M6
 
0.1%
R6
 
0.1%
Other values (3)10
 
0.2%
ValueCountFrequency (%)
92891
37.1%
12681
34.4%
0588
 
7.5%
3516
 
6.6%
4467
 
6.0%
2434
 
5.6%
876
 
1.0%
675
 
1.0%
550
 
0.6%
724
 
0.3%
ValueCountFrequency (%)
.485
99.4%
,3
 
0.6%
ValueCountFrequency (%)
4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common8294
59.2%
Latin5712
40.8%

Most frequent character per script

ValueCountFrequency (%)
A4135
72.4%
B818
 
14.3%
J453
 
7.9%
C232
 
4.1%
K18
 
0.3%
N13
 
0.2%
D11
 
0.2%
L10
 
0.2%
M6
 
0.1%
R6
 
0.1%
Other values (3)10
 
0.2%
ValueCountFrequency (%)
92891
34.9%
12681
32.3%
0588
 
7.1%
3516
 
6.2%
.485
 
5.8%
4467
 
5.6%
2434
 
5.2%
876
 
0.9%
675
 
0.9%
550
 
0.6%
Other values (3)31
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII14006
100.0%

Most frequent character per block

ValueCountFrequency (%)
A4135
29.5%
92891
20.6%
12681
19.1%
B818
 
5.8%
0588
 
4.2%
3516
 
3.7%
.485
 
3.5%
4467
 
3.3%
J453
 
3.2%
2434
 
3.1%
Other values (16)538
 
3.8%

igg
Real number (ℝ)

MISSING

Distinct1779
Distinct (%)43.8%
Missing45041
Missing (%)91.7%
Infinite0
Infinite (%)0.0%
Mean9.919446726
Minimum-2.6
Maximum149.31
Zeros9
Zeros (%)< 0.1%
Memory size383.7 KiB
2021-02-12T10:48:15.708859image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-2.6
5-th percentile-0.08
Q10.74
median2.19
Q38.82
95-th percentile54.97
Maximum149.31
Range151.91
Interquartile range (IQR)8.08

Descriptive statistics

Standard deviation18.47542777
Coefficient of variation (CV)1.862546197
Kurtosis10.14407429
Mean9.919446726
Median Absolute Deviation (MAD)1.928181818
Skewness2.982546161
Sum40282.87315
Variance341.3414314
MonotocityNot monotonic
2021-02-12T10:48:15.824034image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1819
 
< 0.1%
0.4918
 
< 0.1%
1.0817
 
< 0.1%
0.9917
 
< 0.1%
0.116
 
< 0.1%
0.3815
 
< 0.1%
0.6115
 
< 0.1%
0.4415
 
< 0.1%
0.5115
 
< 0.1%
115
 
< 0.1%
Other values (1769)3899
 
7.9%
(Missing)45041
91.7%
ValueCountFrequency (%)
-2.61
< 0.1%
-2.421
< 0.1%
-2.261
< 0.1%
-2.231
< 0.1%
-2.171
< 0.1%
ValueCountFrequency (%)
149.311
< 0.1%
142.11
< 0.1%
130.781
< 0.1%
129.751
< 0.1%
124.781
< 0.1%

igg_interpretation
Categorical

MISSING

Distinct3
Distinct (%)0.1%
Missing45041
Missing (%)91.7%
Memory size383.7 KiB
Negative
3035 
Positive
932 
Equivocal
 
94

Length

Max length9
Median length8
Mean length8.023147008
Min length8

Characters and Unicode

Total characters32582
Distinct characters15
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNegative
2nd rowPositive
3rd rowNegative
4th rowNegative
5th rowNegative
ValueCountFrequency (%)
Negative3035
 
6.2%
Positive932
 
1.9%
Equivocal94
 
0.2%
(Missing)45041
91.7%
2021-02-12T10:48:16.032405image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-02-12T10:48:16.087792image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
negative3035
74.7%
positive932
 
23.0%
equivocal94
 
2.3%

Most occurring characters

ValueCountFrequency (%)
e7002
21.5%
i4993
15.3%
v4061
12.5%
t3967
12.2%
a3129
9.6%
N3035
9.3%
g3035
9.3%
o1026
 
3.1%
P932
 
2.9%
s932
 
2.9%
Other values (5)470
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter28521
87.5%
Uppercase Letter4061
 
12.5%

Most frequent character per category

ValueCountFrequency (%)
e7002
24.6%
i4993
17.5%
v4061
14.2%
t3967
13.9%
a3129
11.0%
g3035
10.6%
o1026
 
3.6%
s932
 
3.3%
q94
 
0.3%
u94
 
0.3%
Other values (2)188
 
0.7%
ValueCountFrequency (%)
N3035
74.7%
P932
 
23.0%
E94
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
Latin32582
100.0%

Most frequent character per script

ValueCountFrequency (%)
e7002
21.5%
i4993
15.3%
v4061
12.5%
t3967
12.2%
a3129
9.6%
N3035
9.3%
g3035
9.3%
o1026
 
3.1%
P932
 
2.9%
s932
 
2.9%
Other values (5)470
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII32582
100.0%

Most frequent character per block

ValueCountFrequency (%)
e7002
21.5%
i4993
15.3%
v4061
12.5%
t3967
12.2%
a3129
9.6%
N3035
9.3%
g3035
9.3%
o1026
 
3.1%
P932
 
2.9%
s932
 
2.9%
Other values (5)470
 
1.4%

igm
Real number (ℝ)

MISSING

Distinct2003
Distinct (%)49.3%
Missing45041
Missing (%)91.7%
Infinite0
Infinite (%)0.0%
Mean11.13357609
Minimum-3.66
Maximum400.45
Zeros1
Zeros (%)< 0.1%
Memory size383.7 KiB
2021-02-12T10:48:16.171335image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-3.66
5-th percentile0.19
Q11.68
median3.52
Q312.65
95-th percentile47.96
Maximum400.45
Range404.11
Interquartile range (IQR)10.97

Descriptive statistics

Standard deviation21.89616817
Coefficient of variation (CV)1.966678811
Kurtosis94.1029252
Mean11.13357609
Median Absolute Deviation (MAD)2.45
Skewness7.438908275
Sum45213.45248
Variance479.4421807
MonotocityNot monotonic
2021-02-12T10:48:16.288874image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.5717
 
< 0.1%
1.5517
 
< 0.1%
1.6315
 
< 0.1%
1.5115
 
< 0.1%
1.8514
 
< 0.1%
1.6613
 
< 0.1%
1.6812
 
< 0.1%
1.0112
 
< 0.1%
212
 
< 0.1%
1.6112
 
< 0.1%
Other values (1993)3922
 
8.0%
(Missing)45041
91.7%
ValueCountFrequency (%)
-3.661
< 0.1%
-3.581
< 0.1%
-3.561
< 0.1%
-3.441
< 0.1%
-3.381
< 0.1%
ValueCountFrequency (%)
400.451
< 0.1%
350.261
< 0.1%
341.481
< 0.1%
328.711
< 0.1%
313.351
< 0.1%

igm_interpretation
Categorical

MISSING

Distinct3
Distinct (%)0.1%
Missing45041
Missing (%)91.7%
Memory size383.7 KiB
Negative
2841 
Positive
1085 
Equivocal
 
135

Length

Max length9
Median length8
Mean length8.033243044
Min length8

Characters and Unicode

Total characters32623
Distinct characters15
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNegative
2nd rowPositive
3rd rowNegative
4th rowNegative
5th rowNegative
ValueCountFrequency (%)
Negative2841
 
5.8%
Positive1085
 
2.2%
Equivocal135
 
0.3%
(Missing)45041
91.7%
2021-02-12T10:48:16.483604image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-02-12T10:48:16.537403image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
negative2841
70.0%
positive1085
 
26.7%
equivocal135
 
3.3%

Most occurring characters

ValueCountFrequency (%)
e6767
20.7%
i5146
15.8%
v4061
12.4%
t3926
12.0%
a2976
9.1%
N2841
8.7%
g2841
8.7%
o1220
 
3.7%
P1085
 
3.3%
s1085
 
3.3%
Other values (5)675
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter28562
87.6%
Uppercase Letter4061
 
12.4%

Most frequent character per category

ValueCountFrequency (%)
e6767
23.7%
i5146
18.0%
v4061
14.2%
t3926
13.7%
a2976
10.4%
g2841
9.9%
o1220
 
4.3%
s1085
 
3.8%
q135
 
0.5%
u135
 
0.5%
Other values (2)270
 
0.9%
ValueCountFrequency (%)
N2841
70.0%
P1085
 
26.7%
E135
 
3.3%

Most occurring scripts

ValueCountFrequency (%)
Latin32623
100.0%

Most frequent character per script

ValueCountFrequency (%)
e6767
20.7%
i5146
15.8%
v4061
12.4%
t3926
12.0%
a2976
9.1%
N2841
8.7%
g2841
8.7%
o1220
 
3.7%
P1085
 
3.3%
s1085
 
3.3%
Other values (5)675
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII32623
100.0%

Most frequent character per block

ValueCountFrequency (%)
e6767
20.7%
i5146
15.8%
v4061
12.4%
t3926
12.0%
a2976
9.1%
N2841
8.7%
g2841
8.7%
o1220
 
3.7%
P1085
 
3.3%
s1085
 
3.3%
Other values (5)675
 
2.1%

jaundice
Boolean

MISSING

Distinct2
Distinct (%)2.0%
Missing49000
Missing (%)99.8%
Memory size383.7 KiB
False
 
87
True
 
15
(Missing)
49000 
ValueCountFrequency (%)
False87
 
0.2%
True15
 
< 0.1%
(Missing)49000
99.8%
2021-02-12T10:48:16.575514image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

lethargy
Boolean

MISSING

Distinct2
Distinct (%)< 0.1%
Missing21567
Missing (%)43.9%
Memory size383.7 KiB
False
26891 
True
 
644
(Missing)
21567 
ValueCountFrequency (%)
False26891
54.8%
True644
 
1.3%
(Missing)21567
43.9%
2021-02-12T10:48:16.612206image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

liver_acute
Boolean

MISSING

Distinct2
Distinct (%)13.3%
Missing49087
Missing (%)> 99.9%
Memory size383.7 KiB
True
 
10
False
 
5
(Missing)
49087 
ValueCountFrequency (%)
True10
 
< 0.1%
False5
 
< 0.1%
(Missing)49087
> 99.9%
2021-02-12T10:48:16.644651image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

lymphocytes
Real number (ℝ≥0)

MISSING

Distinct1089
Distinct (%)13.5%
Missing41006
Missing (%)83.5%
Infinite0
Infinite (%)0.0%
Mean2.172581176
Minimum0.016
Maximum12.6
Zeros0
Zeros (%)0.0%
Memory size383.7 KiB
2021-02-12T10:48:16.716228image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0.016
5-th percentile0.6
Q11.1
median1.79
Q32.8
95-th percentile5.09
Maximum12.6
Range12.584
Interquartile range (IQR)1.7

Descriptive statistics

Standard deviation1.489704661
Coefficient of variation (CV)0.6856842345
Kurtosis4.635377087
Mean2.172581176
Median Absolute Deviation (MAD)0.79
Skewness1.751792601
Sum17589.2172
Variance2.219219976
MonotocityNot monotonic
2021-02-12T10:48:16.832767image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.2138
 
0.3%
1.3136
 
0.3%
1126
 
0.3%
1.7125
 
0.3%
0.9124
 
0.3%
1.4122
 
0.2%
1.1118
 
0.2%
1.5118
 
0.2%
1.8116
 
0.2%
0.8106
 
0.2%
Other values (1079)6867
 
14.0%
(Missing)41006
83.5%
ValueCountFrequency (%)
0.0161
< 0.1%
0.041
< 0.1%
0.0482
< 0.1%
0.0531
< 0.1%
0.0581
< 0.1%
ValueCountFrequency (%)
12.61
< 0.1%
12.461
< 0.1%
12.161
< 0.1%
11.81
< 0.1%
11.121
< 0.1%

lymphocytes_percent
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct956
Distinct (%)11.8%
Missing41030
Missing (%)83.6%
Infinite0
Infinite (%)0.0%
Mean27.03719066
Minimum0.234
Maximum90.2
Zeros0
Zeros (%)0.0%
Memory size383.7 KiB
2021-02-12T10:48:16.945409image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0.234
5-th percentile8.2
Q116
median24.5
Q335.7
95-th percentile54.6
Maximum90.2
Range89.966
Interquartile range (IQR)19.7

Descriptive statistics

Standard deviation14.40317403
Coefficient of variation (CV)0.5327171086
Kurtosis0.3687916755
Mean27.03719066
Median Absolute Deviation (MAD)9.4
Skewness0.8148987963
Sum218244.203
Variance207.4514222
MonotocityNot monotonic
2021-02-12T10:48:17.056564image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23.437
 
0.1%
21.837
 
0.1%
21.435
 
0.1%
18.734
 
0.1%
19.934
 
0.1%
20.632
 
0.1%
1832
 
0.1%
22.532
 
0.1%
14.531
 
0.1%
1631
 
0.1%
Other values (946)7737
 
15.8%
(Missing)41030
83.6%
ValueCountFrequency (%)
0.2341
< 0.1%
0.2471
< 0.1%
0.6171
< 0.1%
0.7831
< 0.1%
0.8711
< 0.1%
ValueCountFrequency (%)
90.21
< 0.1%
86.91
< 0.1%
84.21
< 0.1%
83.21
< 0.1%
81.51
< 0.1%

monocytes
Real number (ℝ≥0)

MISSING
SKEWED

Distinct949
Distinct (%)11.7%
Missing41006
Missing (%)83.5%
Infinite0
Infinite (%)0.0%
Mean0.8163214427
Minimum0.004
Maximum374
Zeros0
Zeros (%)0.0%
Memory size383.7 KiB
2021-02-12T10:48:17.163815image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0.004
5-th percentile0.2
Q10.41575
median0.66
Q31
95-th percentile1.6725
Maximum374
Range373.996
Interquartile range (IQR)0.58425

Descriptive statistics

Standard deviation4.178178711
Coefficient of variation (CV)5.118300822
Kurtosis7864.696982
Mean0.8163214427
Median Absolute Deviation (MAD)0.26
Skewness88.04653391
Sum6608.9384
Variance17.45717734
MonotocityNot monotonic
2021-02-12T10:48:17.281943image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.5332
 
0.7%
0.4324
 
0.7%
0.3296
 
0.6%
0.6283
 
0.6%
0.7274
 
0.6%
0.8222
 
0.5%
0.9188
 
0.4%
0.2156
 
0.3%
1143
 
0.3%
1.1127
 
0.3%
Other values (939)5751
 
11.7%
(Missing)41006
83.5%
ValueCountFrequency (%)
0.0041
< 0.1%
0.0061
< 0.1%
0.0071
< 0.1%
0.0081
< 0.1%
0.012
< 0.1%
ValueCountFrequency (%)
3741
< 0.1%
5.81
< 0.1%
5.71
< 0.1%
51
< 0.1%
4.71
< 0.1%

monocytes_percent
Real number (ℝ≥0)

MISSING

Distinct789
Distinct (%)9.7%
Missing41006
Missing (%)83.5%
Infinite0
Infinite (%)0.0%
Mean9.208251112
Minimum0.076
Maximum34.9
Zeros0
Zeros (%)0.0%
Memory size383.7 KiB
2021-02-12T10:48:17.402492image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0.076
5-th percentile4
Q16.5
median8.54
Q311.2
95-th percentile16.5
Maximum34.9
Range34.824
Interquartile range (IQR)4.7

Descriptive statistics

Standard deviation4.000307019
Coefficient of variation (CV)0.4344263607
Kurtosis2.5578977
Mean9.208251112
Median Absolute Deviation (MAD)2.26
Skewness1.113847843
Sum74550.001
Variance16.00245624
MonotocityNot monotonic
2021-02-12T10:48:17.525059image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.798
 
0.2%
7.596
 
0.2%
8.193
 
0.2%
8.390
 
0.2%
790
 
0.2%
7.489
 
0.2%
6.389
 
0.2%
7.987
 
0.2%
7.287
 
0.2%
6.987
 
0.2%
Other values (779)7190
 
14.6%
(Missing)41006
83.5%
ValueCountFrequency (%)
0.0761
< 0.1%
0.1061
< 0.1%
0.1111
< 0.1%
0.1121
< 0.1%
0.1241
< 0.1%
ValueCountFrequency (%)
34.91
< 0.1%
34.11
< 0.1%
33.51
< 0.1%
32.11
< 0.1%
31.91
< 0.1%

movement
Boolean

MISSING

Distinct2
Distinct (%)< 0.1%
Missing43502
Missing (%)88.6%
Memory size383.7 KiB
False
 
3385
True
 
2215
(Missing)
43502 
ValueCountFrequency (%)
False3385
 
6.9%
True2215
 
4.5%
(Missing)43502
88.6%
2021-02-12T10:48:17.597858image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

nasal_packing
Boolean

MISSING

Distinct2
Distinct (%)0.1%
Missing47536
Missing (%)96.8%
Memory size383.7 KiB
False
 
1564
True
 
2
(Missing)
47536 
ValueCountFrequency (%)
False1564
 
3.2%
True2
 
< 0.1%
(Missing)47536
96.8%
2021-02-12T10:48:17.643094image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

neutrophils
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct1368
Distinct (%)16.9%
Missing41007
Missing (%)83.5%
Infinite0
Infinite (%)0.0%
Mean5.729247251
Minimum0.144
Maximum59
Zeros0
Zeros (%)0.0%
Memory size383.7 KiB
2021-02-12T10:48:17.730346image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0.144
5-th percentile1.4
Q12.87
median4.75
Q37.57
95-th percentile13.2
Maximum59
Range58.856
Interquartile range (IQR)4.7

Descriptive statistics

Standard deviation3.975540053
Coefficient of variation (CV)0.6939026854
Kurtosis8.321089458
Mean5.729247251
Median Absolute Deviation (MAD)2.17
Skewness1.902344851
Sum46378.2565
Variance15.80491871
MonotocityNot monotonic
2021-02-12T10:48:17.858730image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.157
 
0.1%
2.757
 
0.1%
2.456
 
0.1%
3.253
 
0.1%
3.352
 
0.1%
2.551
 
0.1%
3.548
 
0.1%
4.348
 
0.1%
2.647
 
0.1%
3.647
 
0.1%
Other values (1358)7579
 
15.4%
(Missing)41007
83.5%
ValueCountFrequency (%)
0.1441
< 0.1%
0.271
< 0.1%
0.351
< 0.1%
0.3791
< 0.1%
0.461
< 0.1%
ValueCountFrequency (%)
591
< 0.1%
411
< 0.1%
38.431
< 0.1%
33.21
< 0.1%
31.11
< 0.1%

neutrophils_percent
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct761
Distinct (%)9.4%
Missing41030
Missing (%)83.6%
Infinite0
Infinite (%)0.0%
Mean62.51500496
Minimum5.6
Maximum94.3
Zeros0
Zeros (%)0.0%
Memory size383.7 KiB
2021-02-12T10:48:17.984026image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum5.6
5-th percentile33.3
Q152.8
median64.3
Q374.4
95-th percentile84.4
Maximum94.3
Range88.7
Interquartile range (IQR)21.6

Descriptive statistics

Standard deviation15.49205502
Coefficient of variation (CV)0.2478133855
Kurtosis-0.1142386743
Mean62.51500496
Median Absolute Deviation (MAD)10.7
Skewness-0.5666227476
Sum504621.12
Variance240.0037689
MonotocityNot monotonic
2021-02-12T10:48:18.106472image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
75.333
 
0.1%
73.831
 
0.1%
6931
 
0.1%
63.830
 
0.1%
74.729
 
0.1%
69.529
 
0.1%
77.728
 
0.1%
74.128
 
0.1%
64.628
 
0.1%
70.128
 
0.1%
Other values (751)7777
 
15.8%
(Missing)41030
83.6%
ValueCountFrequency (%)
5.61
< 0.1%
9.21
< 0.1%
9.81
< 0.1%
10.31
< 0.1%
10.71
< 0.1%
ValueCountFrequency (%)
94.31
< 0.1%
93.71
< 0.1%
93.31
< 0.1%
93.21
< 0.1%
931
< 0.1%

ns1_platelia_interpretation
Categorical

MISSING

Distinct3
Distinct (%)0.1%
Missing43218
Missing (%)88.0%
Memory size383.7 KiB
Negative
5565 
Positive
 
280
Equivocal
 
39

Length

Max length9
Median length8
Mean length8.006628144
Min length8

Characters and Unicode

Total characters47111
Distinct characters15
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPositive
2nd rowNegative
3rd rowNegative
4th rowNegative
5th rowNegative
ValueCountFrequency (%)
Negative5565
 
11.3%
Positive280
 
0.6%
Equivocal39
 
0.1%
(Missing)43218
88.0%
2021-02-12T10:48:18.321421image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-02-12T10:48:18.383336image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
negative5565
94.6%
positive280
 
4.8%
equivocal39
 
0.7%

Most occurring characters

ValueCountFrequency (%)
e11410
24.2%
i6164
13.1%
v5884
12.5%
t5845
12.4%
a5604
11.9%
N5565
11.8%
g5565
11.8%
o319
 
0.7%
P280
 
0.6%
s280
 
0.6%
Other values (5)195
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter41227
87.5%
Uppercase Letter5884
 
12.5%

Most frequent character per category

ValueCountFrequency (%)
e11410
27.7%
i6164
15.0%
v5884
14.3%
t5845
14.2%
a5604
13.6%
g5565
13.5%
o319
 
0.8%
s280
 
0.7%
q39
 
0.1%
u39
 
0.1%
Other values (2)78
 
0.2%
ValueCountFrequency (%)
N5565
94.6%
P280
 
4.8%
E39
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
Latin47111
100.0%

Most frequent character per script

ValueCountFrequency (%)
e11410
24.2%
i6164
13.1%
v5884
12.5%
t5845
12.4%
a5604
11.9%
N5565
11.8%
g5565
11.8%
o319
 
0.7%
P280
 
0.6%
s280
 
0.6%
Other values (5)195
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII47111
100.0%

Most frequent character per block

ValueCountFrequency (%)
e11410
24.2%
i6164
13.1%
v5884
12.5%
t5845
12.4%
a5604
11.9%
N5565
11.8%
g5565
11.8%
o319
 
0.7%
P280
 
0.6%
s280
 
0.6%
Other values (5)195
 
0.4%
Distinct2
Distinct (%)< 0.1%
Missing41001
Missing (%)83.5%
Memory size383.7 KiB
True
8100 
False
 
1
(Missing)
41001 
ValueCountFrequency (%)
True8100
 
16.5%
False1
 
< 0.1%
(Missing)41001
83.5%
2021-02-12T10:48:18.429084image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing41001
Missing (%)83.5%
Memory size383.7 KiB
False
6506 
True
 
1595
(Missing)
41001 
ValueCountFrequency (%)
False6506
 
13.2%
True1595
 
3.2%
(Missing)41001
83.5%
2021-02-12T10:48:18.463048image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size383.7 KiB
Lab-confirmed Dengue
49101 
Not Dengue
 
1

Length

Max length20
Median length20
Mean length19.99979634
Min length10

Characters and Unicode

Total characters982030
Distinct characters19
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowLab-confirmed Dengue
2nd rowLab-confirmed Dengue
3rd rowLab-confirmed Dengue
4th rowLab-confirmed Dengue
5th rowLab-confirmed Dengue
ValueCountFrequency (%)
Lab-confirmed Dengue49101
> 99.9%
Not Dengue1
 
< 0.1%
2021-02-12T10:48:18.611329image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-02-12T10:48:18.661345image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
dengue49102
50.0%
lab-confirmed49101
50.0%
not1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e147305
15.0%
n98203
 
10.0%
o49102
 
5.0%
49102
 
5.0%
D49102
 
5.0%
g49102
 
5.0%
u49102
 
5.0%
L49101
 
5.0%
a49101
 
5.0%
b49101
 
5.0%
Other values (9)343709
35.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter785623
80.0%
Uppercase Letter98204
 
10.0%
Space Separator49102
 
5.0%
Dash Punctuation49101
 
5.0%

Most frequent character per category

ValueCountFrequency (%)
e147305
18.8%
n98203
12.5%
o49102
 
6.3%
g49102
 
6.3%
u49102
 
6.3%
a49101
 
6.2%
b49101
 
6.2%
c49101
 
6.2%
f49101
 
6.2%
i49101
 
6.2%
Other values (4)147304
18.7%
ValueCountFrequency (%)
D49102
50.0%
L49101
50.0%
N1
 
< 0.1%
ValueCountFrequency (%)
-49101
100.0%
ValueCountFrequency (%)
49102
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin883827
90.0%
Common98203
 
10.0%

Most frequent character per script

ValueCountFrequency (%)
e147305
16.7%
n98203
11.1%
o49102
 
5.6%
D49102
 
5.6%
g49102
 
5.6%
u49102
 
5.6%
L49101
 
5.6%
a49101
 
5.6%
b49101
 
5.6%
c49101
 
5.6%
Other values (7)245507
27.8%
ValueCountFrequency (%)
49102
50.0%
-49101
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII982030
100.0%

Most frequent character per block

ValueCountFrequency (%)
e147305
15.0%
n98203
 
10.0%
o49102
 
5.0%
49102
 
5.0%
D49102
 
5.0%
g49102
 
5.0%
u49102
 
5.0%
L49101
 
5.0%
a49101
 
5.0%
b49101
 
5.0%
Other values (9)343709
35.0%

pcr_dengue_load
Real number (ℝ≥0)

MISSING
SKEWED
ZEROS

Distinct1825
Distinct (%)22.5%
Missing41003
Missing (%)83.5%
Infinite0
Infinite (%)0.0%
Mean170839081.2
Minimum0
Maximum1.68452 × 1011
Zeros6032
Zeros (%)12.3%
Memory size383.7 KiB
2021-02-12T10:48:18.747035image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31830.357143
95-th percentile261904761.9
Maximum1.68452 × 1011
Range1.68452 × 1011
Interquartile range (IQR)1830.357143

Descriptive statistics

Standard deviation2808985386
Coefficient of variation (CV)16.44228806
Kurtosis2097.164447
Mean170839081.2
Median Absolute Deviation (MAD)0
Skewness41.74880205
Sum1.383625719 × 1012
Variance7.890398897 × 1018
MonotocityNot monotonic
2021-02-12T10:48:18.870501image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
06032
 
12.3%
6250006
 
< 0.1%
75000004
 
< 0.1%
6250000004
 
< 0.1%
7619000004
 
< 0.1%
6786003
 
< 0.1%
61904.76193
 
< 0.1%
7916666.6673
 
< 0.1%
7380952.3813
 
< 0.1%
10357142863
 
< 0.1%
Other values (1815)2034
 
4.1%
(Missing)41003
83.5%
ValueCountFrequency (%)
06032
12.3%
67.861
 
< 0.1%
78.571428571
 
< 0.1%
85.1191
 
< 0.1%
89.881
 
< 0.1%
ValueCountFrequency (%)
1.68452 × 10111
< 0.1%
1.11905 × 10111
< 0.1%
9.345238095 × 10101
< 0.1%
6.30952381 × 10101
< 0.1%
4.101190476 × 10101
< 0.1%
Distinct9
Distinct (%)< 0.1%
Missing13
Missing (%)< 0.1%
Memory size383.7 KiB
<LOD
35776 
DENV-1
5430 
DENV-4
3690 
DENV-2
 
2835
DENV-3
 
1269
Other values (4)
 
89

Length

Max length13
Median length4
Mean length4.555093809
Min length4

Characters and Unicode

Total characters223605
Distinct characters13
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDENV-2
2nd rowDENV-2
3rd rowDENV-2
4th rowDENV-2
5th rowDENV-2
ValueCountFrequency (%)
<LOD35776
72.9%
DENV-15430
 
11.1%
DENV-43690
 
7.5%
DENV-22835
 
5.8%
DENV-31269
 
2.6%
DENV-1,DENV-245
 
0.1%
DENV-1,DENV-433
 
0.1%
DENV-3,DENV-46
 
< 0.1%
DENV-1,DENV-35
 
< 0.1%
(Missing)13
 
< 0.1%
2021-02-12T10:48:19.076828image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-02-12T10:48:19.134774image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
lod35776
72.9%
denv-15430
 
11.1%
denv-43690
 
7.5%
denv-22835
 
5.8%
denv-31269
 
2.6%
denv-1,denv-245
 
0.1%
denv-1,denv-433
 
0.1%
denv-3,denv-46
 
< 0.1%
denv-1,denv-35
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
D49178
22.0%
<35776
16.0%
L35776
16.0%
O35776
16.0%
E13402
 
6.0%
N13402
 
6.0%
V13402
 
6.0%
-13402
 
6.0%
15513
 
2.5%
43729
 
1.7%
Other values (3)4249
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter160936
72.0%
Math Symbol35776
 
16.0%
Dash Punctuation13402
 
6.0%
Decimal Number13402
 
6.0%
Other Punctuation89
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
D49178
30.6%
L35776
22.2%
O35776
22.2%
E13402
 
8.3%
N13402
 
8.3%
V13402
 
8.3%
ValueCountFrequency (%)
15513
41.1%
43729
27.8%
22880
21.5%
31280
 
9.6%
ValueCountFrequency (%)
-13402
100.0%
ValueCountFrequency (%)
<35776
100.0%
ValueCountFrequency (%)
,89
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin160936
72.0%
Common62669
 
28.0%

Most frequent character per script

ValueCountFrequency (%)
<35776
57.1%
-13402
 
21.4%
15513
 
8.8%
43729
 
6.0%
22880
 
4.6%
31280
 
2.0%
,89
 
0.1%
ValueCountFrequency (%)
D49178
30.6%
L35776
22.2%
O35776
22.2%
E13402
 
8.3%
N13402
 
8.3%
V13402
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII223605
100.0%

Most frequent character per block

ValueCountFrequency (%)
D49178
22.0%
<35776
16.0%
L35776
16.0%
O35776
16.0%
E13402
 
6.0%
N13402
 
6.0%
V13402
 
6.0%
-13402
 
6.0%
15513
 
2.5%
43729
 
1.7%
Other values (3)4249
 
1.9%

platelet_no
Real number (ℝ≥0)

MISSING

Distinct14
Distinct (%)0.9%
Missing47536
Missing (%)96.8%
Infinite0
Infinite (%)0.0%
Mean2.517241379
Minimum1
Maximum27
Zeros0
Zeros (%)0.0%
Memory size383.7 KiB
2021-02-12T10:48:19.214682image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile6
Maximum27
Range26
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.807869642
Coefficient of variation (CV)0.7181947891
Kurtosis31.42207614
Mean2.517241379
Median Absolute Deviation (MAD)1
Skewness3.533555517
Sum3942
Variance3.268392641
MonotocityNot monotonic
2021-02-12T10:48:19.301679image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
1524
 
1.1%
2427
 
0.9%
3268
 
0.5%
4173
 
0.4%
593
 
0.2%
641
 
0.1%
721
 
< 0.1%
88
 
< 0.1%
93
 
< 0.1%
103
 
< 0.1%
Other values (4)5
 
< 0.1%
(Missing)47536
96.8%
ValueCountFrequency (%)
1524
1.1%
2427
0.9%
3268
0.5%
4173
 
0.4%
593
 
0.2%
ValueCountFrequency (%)
271
 
< 0.1%
211
 
< 0.1%
152
< 0.1%
111
 
< 0.1%
103
< 0.1%

platelets
Boolean

MISSING

Distinct2
Distinct (%)0.1%
Missing47536
Missing (%)96.8%
Memory size383.7 KiB
False
 
1560
True
 
6
(Missing)
47536 
ValueCountFrequency (%)
False1560
 
3.2%
True6
 
< 0.1%
(Missing)47536
96.8%
2021-02-12T10:48:19.352889image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

pleural_effusion
Boolean

MISSING

Distinct2
Distinct (%)< 0.1%
Missing38935
Missing (%)79.3%
Memory size383.7 KiB
False
9937 
True
 
230
(Missing)
38935 
ValueCountFrequency (%)
False9937
 
20.2%
True230
 
0.5%
(Missing)38935
79.3%
2021-02-12T10:48:19.391126image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

plt
Real number (ℝ≥0)

MISSING

Distinct556
Distinct (%)5.8%
Missing39443
Missing (%)80.3%
Infinite0
Infinite (%)0.0%
Mean217.1647686
Minimum7
Maximum829
Zeros0
Zeros (%)0.0%
Memory size383.7 KiB
2021-02-12T10:48:19.475648image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile75
Q1160
median216
Q3270
95-th percentile363
Maximum829
Range822
Interquartile range (IQR)110

Descriptive statistics

Standard deviation87.85996283
Coefficient of variation (CV)0.4045774248
Kurtosis1.075836047
Mean217.1647686
Median Absolute Deviation (MAD)55
Skewness0.4047913261
Sum2097594.5
Variance7719.373069
MonotocityNot monotonic
2021-02-12T10:48:19.582635image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22472
 
0.1%
23561
 
0.1%
23660
 
0.1%
19557
 
0.1%
19857
 
0.1%
20457
 
0.1%
20856
 
0.1%
22056
 
0.1%
22856
 
0.1%
23455
 
0.1%
Other values (546)9072
 
18.5%
(Missing)39443
80.3%
ValueCountFrequency (%)
73
< 0.1%
81
 
< 0.1%
91
 
< 0.1%
102
< 0.1%
111
 
< 0.1%
ValueCountFrequency (%)
8291
< 0.1%
7511
< 0.1%
7091
< 0.1%
6911
< 0.1%
6541
< 0.1%

pulse
Real number (ℝ≥0)

MISSING

Distinct30
Distinct (%)26.8%
Missing48990
Missing (%)99.8%
Infinite0
Infinite (%)0.0%
Mean114.5714286
Minimum80
Maximum165
Zeros0
Zeros (%)0.0%
Memory size383.7 KiB
2021-02-12T10:48:19.685883image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum80
5-th percentile96
Q1102
median117
Q3120
95-th percentile134.7
Maximum165
Range85
Interquartile range (IQR)18

Descriptive statistics

Standard deviation13.45577627
Coefficient of variation (CV)0.1174444313
Kurtosis0.9453342865
Mean114.5714286
Median Absolute Deviation (MAD)9
Skewness0.4083712557
Sum12832
Variance181.0579151
MonotocityNot monotonic
2021-02-12T10:48:19.777179image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
12031
 
0.1%
10019
 
< 0.1%
11014
 
< 0.1%
13011
 
< 0.1%
1263
 
< 0.1%
1063
 
< 0.1%
1142
 
< 0.1%
1402
 
< 0.1%
902
 
< 0.1%
1082
 
< 0.1%
Other values (20)23
 
< 0.1%
(Missing)48990
99.8%
ValueCountFrequency (%)
801
< 0.1%
902
< 0.1%
921
< 0.1%
941
< 0.1%
962
< 0.1%
ValueCountFrequency (%)
1651
< 0.1%
1451
< 0.1%
1441
< 0.1%
1402
< 0.1%
1381
< 0.1%

pulse_status
Categorical

MISSING

Distinct3
Distinct (%)2.6%
Missing48988
Missing (%)99.8%
Memory size383.7 KiB
Weak
89 
Strong
21 
Not done / Not detected
 
4

Length

Max length23
Median length4
Mean length5.035087719
Min length4

Characters and Unicode

Total characters574
Distinct characters15
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWeak
2nd rowWeak
3rd rowWeak
4th rowWeak
5th rowWeak
ValueCountFrequency (%)
Weak89
 
0.2%
Strong21
 
< 0.1%
Not done / Not detected4
 
< 0.1%
(Missing)48988
99.8%
2021-02-12T10:48:19.965810image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-02-12T10:48:20.025401image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
weak89
68.5%
strong21
 
16.2%
not8
 
6.2%
4
 
3.1%
detected4
 
3.1%
done4
 
3.1%

Most occurring characters

ValueCountFrequency (%)
e105
18.3%
W89
15.5%
a89
15.5%
k89
15.5%
t37
 
6.4%
o33
 
5.7%
n25
 
4.4%
S21
 
3.7%
r21
 
3.7%
g21
 
3.7%
Other values (5)44
7.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter436
76.0%
Uppercase Letter118
 
20.6%
Space Separator16
 
2.8%
Other Punctuation4
 
0.7%

Most frequent character per category

ValueCountFrequency (%)
e105
24.1%
a89
20.4%
k89
20.4%
t37
 
8.5%
o33
 
7.6%
n25
 
5.7%
r21
 
4.8%
g21
 
4.8%
d12
 
2.8%
c4
 
0.9%
ValueCountFrequency (%)
W89
75.4%
S21
 
17.8%
N8
 
6.8%
ValueCountFrequency (%)
16
100.0%
ValueCountFrequency (%)
/4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin554
96.5%
Common20
 
3.5%

Most frequent character per script

ValueCountFrequency (%)
e105
19.0%
W89
16.1%
a89
16.1%
k89
16.1%
t37
 
6.7%
o33
 
6.0%
n25
 
4.5%
S21
 
3.8%
r21
 
3.8%
g21
 
3.8%
Other values (3)24
 
4.3%
ValueCountFrequency (%)
16
80.0%
/4
 
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII574
100.0%

Most frequent character per block

ValueCountFrequency (%)
e105
18.3%
W89
15.5%
a89
15.5%
k89
15.5%
t37
 
6.4%
o33
 
5.7%
n25
 
4.4%
S21
 
3.7%
r21
 
3.7%
g21
 
3.7%
Other values (5)44
7.7%

rbc
Boolean

MISSING

Distinct2
Distinct (%)0.1%
Missing47536
Missing (%)96.8%
Memory size383.7 KiB
False
 
1559
True
 
7
(Missing)
47536 
ValueCountFrequency (%)
False1559
 
3.2%
True7
 
< 0.1%
(Missing)47536
96.8%
2021-02-12T10:48:20.063293image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

restlessness
Boolean

MISSING

Distinct2
Distinct (%)< 0.1%
Missing21567
Missing (%)43.9%
Memory size383.7 KiB
False
27529 
True
 
6
(Missing)
21567 
ValueCountFrequency (%)
False27529
56.1%
True6
 
< 0.1%
(Missing)21567
43.9%
2021-02-12T10:48:20.100404image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

sbp
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct11
Distinct (%)10.0%
Missing48992
Missing (%)99.8%
Infinite0
Infinite (%)0.0%
Mean93.72727273
Minimum60
Maximum120
Zeros0
Zeros (%)0.0%
Memory size383.7 KiB
2021-02-12T10:48:20.155015image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum60
5-th percentile80
Q190
median95
Q3100
95-th percentile110
Maximum120
Range60
Interquartile range (IQR)10

Descriptive statistics

Standard deviation10.23656792
Coefficient of variation (CV)0.1092165345
Kurtosis0.456242554
Mean93.72727273
Median Absolute Deviation (MAD)5
Skewness-0.3346212546
Sum10310
Variance104.7873228
MonotocityNot monotonic
2021-02-12T10:48:20.238372image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
10032
 
0.1%
9032
 
0.1%
8013
 
< 0.1%
11011
 
< 0.1%
959
 
< 0.1%
854
 
< 0.1%
1053
 
< 0.1%
752
 
< 0.1%
702
 
< 0.1%
601
 
< 0.1%
(Missing)48992
99.8%
ValueCountFrequency (%)
601
 
< 0.1%
702
 
< 0.1%
752
 
< 0.1%
8013
< 0.1%
854
 
< 0.1%
ValueCountFrequency (%)
1201
 
< 0.1%
11011
 
< 0.1%
1053
 
< 0.1%
10032
0.1%
959
 
< 0.1%

serology_interpretation
Categorical

MISSING

Distinct3
Distinct (%)0.1%
Missing47062
Missing (%)95.8%
Memory size383.7 KiB
Inconclusive
992 
Probable Secondary
747 
Probable primary
301 

Length

Max length18
Median length16
Mean length14.7872549
Min length12

Characters and Unicode

Total characters30166
Distinct characters20
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowProbable Secondary
2nd rowInconclusive
3rd rowInconclusive
4th rowInconclusive
5th rowInconclusive
ValueCountFrequency (%)
Inconclusive992
 
2.0%
Probable Secondary747
 
1.5%
Probable primary301
 
0.6%
(Missing)47062
95.8%
2021-02-12T10:48:20.417082image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-02-12T10:48:20.476624image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
probable1048
33.9%
inconclusive992
32.1%
secondary747
24.2%
primary301
 
9.7%

Most occurring characters

ValueCountFrequency (%)
o2787
 
9.2%
e2787
 
9.2%
c2731
 
9.1%
n2731
 
9.1%
r2397
 
7.9%
b2096
 
6.9%
a2096
 
6.9%
l2040
 
6.8%
i1293
 
4.3%
P1048
 
3.5%
Other values (10)8160
27.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter26331
87.3%
Uppercase Letter2787
 
9.2%
Space Separator1048
 
3.5%

Most frequent character per category

ValueCountFrequency (%)
o2787
10.6%
e2787
10.6%
c2731
10.4%
n2731
10.4%
r2397
9.1%
b2096
8.0%
a2096
8.0%
l2040
7.7%
i1293
 
4.9%
y1048
 
4.0%
Other values (6)4325
16.4%
ValueCountFrequency (%)
P1048
37.6%
I992
35.6%
S747
26.8%
ValueCountFrequency (%)
1048
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin29118
96.5%
Common1048
 
3.5%

Most frequent character per script

ValueCountFrequency (%)
o2787
 
9.6%
e2787
 
9.6%
c2731
 
9.4%
n2731
 
9.4%
r2397
 
8.2%
b2096
 
7.2%
a2096
 
7.2%
l2040
 
7.0%
i1293
 
4.4%
P1048
 
3.6%
Other values (9)7112
24.4%
ValueCountFrequency (%)
1048
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII30166
100.0%

Most frequent character per block

ValueCountFrequency (%)
o2787
 
9.2%
e2787
 
9.2%
c2731
 
9.1%
n2731
 
9.1%
r2397
 
7.9%
b2096
 
6.9%
a2096
 
6.9%
l2040
 
6.8%
i1293
 
4.3%
P1048
 
3.5%
Other values (10)8160
27.1%

shock
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size48.1 KiB
False
48311 
True
 
791
ValueCountFrequency (%)
False48311
98.4%
True791
 
1.6%
2021-02-12T10:48:20.519023image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size48.1 KiB
False
48967 
True
 
135
ValueCountFrequency (%)
False48967
99.7%
True135
 
0.3%
2021-02-12T10:48:20.553723image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

skin_clammy
Boolean

MISSING

Distinct2
Distinct (%)< 0.1%
Missing21567
Missing (%)43.9%
Memory size383.7 KiB
False
27483 
True
 
52
(Missing)
21567 
ValueCountFrequency (%)
False27483
56.0%
True52
 
0.1%
(Missing)21567
43.9%
2021-02-12T10:48:20.596529image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

skin_flush
Boolean

Distinct2
Distinct (%)< 0.1%
Missing8
Missing (%)< 0.1%
Memory size383.7 KiB
False
40739 
True
8355 
(Missing)
 
8
ValueCountFrequency (%)
False40739
83.0%
True8355
 
17.0%
(Missing)8
 
< 0.1%
2021-02-12T10:48:20.628949image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

skin_rash
Boolean

Distinct2
Distinct (%)< 0.1%
Missing8
Missing (%)< 0.1%
Memory size383.7 KiB
False
48081 
True
 
1013
(Missing)
 
8
ValueCountFrequency (%)
False48081
97.9%
True1013
 
2.1%
(Missing)8
 
< 0.1%
2021-02-12T10:48:20.662475image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

vomiting
Boolean

Distinct2
Distinct (%)< 0.1%
Missing8
Missing (%)< 0.1%
Memory size383.7 KiB
False
41711 
True
7383 
(Missing)
 
8
ValueCountFrequency (%)
False41711
84.9%
True7383
 
15.0%
(Missing)8
 
< 0.1%
2021-02-12T10:48:20.699084image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

wbc
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct1594
Distinct (%)19.7%
Missing41006
Missing (%)83.5%
Infinite0
Infinite (%)0.0%
Mean8.76609585
Minimum0.85
Maximum49.58
Zeros0
Zeros (%)0.0%
Memory size383.7 KiB
2021-02-12T10:48:20.772814image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0.85
5-th percentile3.09
Q15.2
median7.71
Q311.2
95-th percentile18.1025
Maximum49.58
Range48.73
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.832291864
Coefficient of variation (CV)0.5512478926
Kurtosis3.693048886
Mean8.76609585
Median Absolute Deviation (MAD)2.835
Skewness1.434827463
Sum70970.312
Variance23.35104465
MonotocityNot monotonic
2021-02-12T10:48:20.899152image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.450
 
0.1%
548
 
0.1%
4.346
 
0.1%
4.744
 
0.1%
8.442
 
0.1%
3.840
 
0.1%
7.239
 
0.1%
5.438
 
0.1%
3.638
 
0.1%
5.738
 
0.1%
Other values (1584)7673
 
15.6%
(Missing)41006
83.5%
ValueCountFrequency (%)
0.851
 
< 0.1%
0.91
 
< 0.1%
1.153
< 0.1%
1.231
 
< 0.1%
1.251
 
< 0.1%
ValueCountFrequency (%)
49.581
< 0.1%
45.41
< 0.1%
44.71
< 0.1%
401
< 0.1%
39.961
< 0.1%

weight
Real number (ℝ≥0)

Distinct279
Distinct (%)0.6%
Missing8
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean24.64102131
Minimum7.2
Maximum91
Zeros0
Zeros (%)0.0%
Memory size383.7 KiB
2021-02-12T10:48:21.013209image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum7.2
5-th percentile11
Q115.2
median21.5
Q331
95-th percentile48
Maximum91
Range83.8
Interquartile range (IQR)15.8

Descriptive statistics

Standard deviation11.90515506
Coefficient of variation (CV)0.4831437347
Kurtosis1.222740622
Mean24.64102131
Median Absolute Deviation (MAD)7.5
Skewness1.135876289
Sum1209726.3
Variance141.732717
MonotocityNot monotonic
2021-02-12T10:48:21.117381image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
202461
 
5.0%
152153
 
4.4%
181643
 
3.3%
251612
 
3.3%
131578
 
3.2%
141553
 
3.2%
171511
 
3.1%
301508
 
3.1%
191468
 
3.0%
221453
 
3.0%
Other values (269)32154
65.5%
ValueCountFrequency (%)
7.26
 
< 0.1%
7.35
 
< 0.1%
7.510
< 0.1%
7.819
< 0.1%
7.95
 
< 0.1%
ValueCountFrequency (%)
914
 
< 0.1%
848
 
< 0.1%
8023
< 0.1%
795
 
< 0.1%
785
 
< 0.1%

day_from_enrolment
Real number (ℝ)

HIGH CORRELATION
SKEWED
ZEROS

Distinct173
Distinct (%)0.4%
Missing8
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean2.221289771
Minimum-4748
Maximum3290
Zeros8100
Zeros (%)16.5%
Memory size383.7 KiB
2021-02-12T10:48:21.224123image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-4748
5-th percentile-2
Q10
median3
Q34
95-th percentile7
Maximum3290
Range8038
Interquartile range (IQR)4

Descriptive statistics

Standard deviation69.31189094
Coefficient of variation (CV)31.20344399
Kurtosis3333.527205
Mean2.221289771
Median Absolute Deviation (MAD)2
Skewness-32.77291566
Sum109052
Variance4804.138226
MonotocityNot monotonic
2021-02-12T10:48:21.337977image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
08100
16.5%
27619
15.5%
37613
15.5%
47450
15.2%
54104
8.4%
-23748
7.6%
62834
 
5.8%
-12602
 
5.3%
71809
 
3.7%
-31324
 
2.7%
Other values (163)1891
 
3.9%
ValueCountFrequency (%)
-47482
 
< 0.1%
-43835
< 0.1%
-43821
 
< 0.1%
-3662
 
< 0.1%
-3656
< 0.1%
ValueCountFrequency (%)
32902
< 0.1%
29241
< 0.1%
29222
< 0.1%
25611
< 0.1%
25581
< 0.1%

day_from_admission
Real number (ℝ)

MISSING
ZEROS

Distinct201
Distinct (%)2.0%
Missing38802
Missing (%)79.0%
Infinite0
Infinite (%)0.0%
Mean-12.08349515
Minimum-4387
Maximum1076
Zeros1594
Zeros (%)3.2%
Memory size383.7 KiB
2021-02-12T10:48:21.450528image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-4387
5-th percentile-3
Q10
median2
Q35
95-th percentile11
Maximum1076
Range5463
Interquartile range (IQR)5

Descriptive statistics

Standard deviation219.0813744
Coefficient of variation (CV)-18.13062957
Kurtosis200.9206788
Mean-12.08349515
Median Absolute Deviation (MAD)3
Skewness-13.8126312
Sum-124460
Variance47996.64863
MonotocityNot monotonic
2021-02-12T10:48:21.559759image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01594
 
3.2%
4917
 
1.9%
3890
 
1.8%
2882
 
1.8%
5845
 
1.7%
1819
 
1.7%
-2705
 
1.4%
-1681
 
1.4%
6672
 
1.4%
-3602
 
1.2%
Other values (191)1693
 
3.4%
(Missing)38802
79.0%
ValueCountFrequency (%)
-43871
< 0.1%
-43831
< 0.1%
-43821
< 0.1%
-32931
< 0.1%
-32911
< 0.1%
ValueCountFrequency (%)
10761
< 0.1%
10441
< 0.1%
9691
< 0.1%
9021
< 0.1%
7961
< 0.1%

day_from_onset
Real number (ℝ)

HIGH CORRELATION
SKEWED
ZEROS

Distinct202
Distinct (%)0.4%
Missing8
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean3.947855135
Minimum-4747
Maximum3293
Zeros8100
Zeros (%)16.5%
Memory size383.7 KiB
2021-02-12T10:48:21.666540image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-4747
5-th percentile0
Q12
median4
Q36
95-th percentile9
Maximum3293
Range8040
Interquartile range (IQR)4

Descriptive statistics

Standard deviation69.30661447
Coefficient of variation (CV)17.55551106
Kurtosis3333.476607
Mean3.947855135
Median Absolute Deviation (MAD)2
Skewness-32.77232447
Sum193816
Variance4803.40681
MonotocityNot monotonic
2021-02-12T10:48:21.784705image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
08100
16.5%
57489
15.3%
46443
13.1%
66267
12.8%
34442
9.0%
24301
8.8%
74256
8.7%
12638
 
5.4%
82486
 
5.1%
91204
 
2.5%
Other values (192)1468
 
3.0%
ValueCountFrequency (%)
-47471
 
< 0.1%
-47461
 
< 0.1%
-43814
< 0.1%
-43801
 
< 0.1%
-43791
 
< 0.1%
ValueCountFrequency (%)
32931
< 0.1%
32911
< 0.1%
29261
< 0.1%
29231
< 0.1%
29221
< 0.1%

Interactions

2021-02-12T10:46:49.889164image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:46:50.051137image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:46:50.170680image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:46:50.292698image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:46:50.407278image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:46:50.566511image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:46:50.687915image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:46:50.816360image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:46:50.938052image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:46:51.059512image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:46:51.183531image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:46:51.317339image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:46:51.435829image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:46:51.565020image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:46:51.678928image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:46:51.804474image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:46:51.927083image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:46:52.036840image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:46:52.151057image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:46:52.272976image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:46:52.380461image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:46:52.466020image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:46:52.565506image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:46:52.692591image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:46:52.801321image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:46:52.918983image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:46:53.040976image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:46:53.156813image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:46:53.285398image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:46:53.392339image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:46:53.500659image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:46:53.607237image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:46:53.705084image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:46:53.778221image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:46:53.861961image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:46:53.991852image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:46:54.113215image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:46:54.214463image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:46:54.315386image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:46:54.423555image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:46:54.530984image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:46:54.635199image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:46:54.733042image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:46:54.836101image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:46:54.929745image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:46:55.056309image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:46:55.134413image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:46:55.239677image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:46:55.306950image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:46:55.375933image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:46:55.481626image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:46:55.581251image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:46:55.692900image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:46:55.791559image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:46:55.890829image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:46:56.009999image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:46:56.120205image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:46:56.211418image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:46:56.877810image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:46:56.956783image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:46:57.015098image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:46:57.086819image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:46:57.175261image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:46:57.259723image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:46:57.346557image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:46:57.428921image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:46:57.517480image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:46:57.604805image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:46:57.691711image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:46:57.781974image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:46:57.866044image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:46:57.939472image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:46:58.032274image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:46:58.108976image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:46:58.197260image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:46:58.258666image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:46:58.325165image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:46:58.413682image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:46:58.496355image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:46:58.583768image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:46:58.668854image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:46:58.752744image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:46:58.843977image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:46:58.927780image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:46:59.009238image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:46:59.091371image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:46:59.170299image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:46:59.231786image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:46:59.304398image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:46:59.393613image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:46:59.485743image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:46:59.570003image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
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2021-02-12T10:46:59.942240image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
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2021-02-12T10:47:00.128977image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
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2021-02-12T10:47:00.373306image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:47:00.460072image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
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2021-02-12T10:47:00.668152image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:47:00.746081image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:47:00.828152image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:47:00.910940image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:47:00.996592image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:47:01.083013image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:47:01.174934image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:47:01.254158image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
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2021-02-12T10:47:01.414982image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
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2021-02-12T10:47:01.548473image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:47:01.642580image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:47:01.735291image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:47:01.826196image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
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2021-02-12T10:47:02.185927image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
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2021-02-12T10:47:02.544615image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
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2021-02-12T10:47:02.831098image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:47:02.919451image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
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2021-02-12T10:47:06.209225image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
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Correlations

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Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
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Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
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Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
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Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

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A simple visualization of nullity by column.
2021-02-12T10:48:04.413089image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-02-12T10:48:07.402665image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-02-12T10:48:09.774010image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

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21-00012010-10-20TrueNaN11.0NaNNaNNaNFalseFalseNaNNaNNaNNaNFalseNaNFalseNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNFalseNaNNaNNaNNaNMaleNaNNaNNaNNaN144.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNLab-confirmed DengueNaNDENV-2NaNNaNFalseNaNNaNNaNNaNNaNNaNNaNTrueFalseNaNFalseFalseTrueNaN34.01.00.03.0
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91-00022010-10-21FalseNaN1.0NaNNaNNaNFalseNaNNaNFalseNaNNaNFalseNaNFalseNaNNaN37.2FalseNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNFalseNaNNaNNaNNaNMaleNaNNaNNaNNaN68.0NaNNaNNaNNaNNaNNaNNaNFalseNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNLab-confirmed DengueNaN<LODNaNNaNNaNNaNNaNNaNNaNFalseNaNNaNFalseFalseFalseTrueFalseFalseNaN9.52.02.04.0

Last rows

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